The complexity of traffic flow patterns significant challenges in predicting traffic green signal timings using conventional methods. Most of conventional methods relied on vehicle counts and speeds. These methods often did not consider crucial factors such as Spatial Occupancy, long-term dependencies, and the non-linear relationships. Recent advancements in Convolutional Neural Networks (CNNs) have enabled better capturing of patterns in traffic data. These advancements are essential for effectively predicting vehicle Green Signal Time by considering accurate detection and tracking, Spatial Occupancy calculation, long-term dependencies, and non-linear relationships in traffic data. The PVD-GSTPS framework has been proposed as an innovative solution for predicting vehicle Green Signal Time with the help of advanced CNN. This framework leverages the capabilities of two fine-tuned object detection models YOLO v8 and Faster R-CNN for precise vehicle detection, while a Byte Sort Tracker monitors the trajectories of detected vehicles. Additionally, a vehicle counting module assesses the number of vehicles in specified areas, and a size assignment process estimates Green Signal Time based on Spatial Occupancy calculations. This study is limited by the fixed duration of the QMUL video dataset utilized. This restricts data availability and complicates the establishment of strong correlations between Green Signal Time and Spatial Occupancy. To mitigate this issue, we utilized a Generative Adversarial Network (GAN) to generate realistic synthetic data. Long Short-Term Memory (LSTM) networks and polynomial regression techniques are utilized to capture the relationships within this dataset. In this study, we used the QMUL dataset to validate our hypothesis. The results demonstrate that our PVD-GSTPS framework significantly outperforms Enhanced YOLO v8, Original YOLO v8, and Faster R-CNN.